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Summary

The standardization of dynamic susceptibility contrast (DSC)-magnetic resonance imaging (MRI) have been confounded by a lack of consensus on DSC-MRI methodology for preventing potential relative cerebral blood volume (CBV) inaccuracies, including the choice of acquisition protocols and postprocessing algorithms. Therefore, a digital reference object (DRO) was developed using physiological and kinetic parameters derived from a patient database, unique voxel-wise 3-dimensional tissue structures, and a validated MRI signal computational approach. The primary, intended use of the DRO is to validate image acquisition and analysis methods for accurately measuring relative cerebral blood volume in glioblastomas [1,2]. The DRO datasets have also been used as part of the QIN-challenge titled “DSC-DRO Challenge” to evaluate multisite rCBV consistency [3], and for systematic assessment of multi-echo DSC-MRI [4].

References:
[1] Semmineh NB, Stokes AM, Bell LC, Boxerman JL, Quarles CC. A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials. Tomography 2017;3:41–9. doi:10.18383/j.tom.2016.00286.
[2] Semmineh NB, Bell LC, Stokes AM, Hu LS, Boxerman JL, Quarles CC. Optimization of acquisition and analysis methods for clinical dynamic susceptibility contrast MRI using a population-based digital reference object. Am J Neuroradiol 2018. doi:10.3174/ajnr.A5827.
[3] Bell LC, Semmineh N, An H, Eldeniz C, Wahl R, Schmainda KM, et al. Evaluating Multisite rCBV Consistency from DSC-MRI Imaging Protocols and Postprocessing Software Across the NCI Quantitative Imaging Network Sites Using a Digital Reference Object (DRO). Tomogr (Ann Arbor, Mich) 2019. doi:10.18383/j.tom.2018.00041.
[4] Stokes AM, Semmineh NB, Nespodzany A, Bell LC, Quarles CC. Systematic assessment of multi-echo dynamic susceptibility contrast MRI using a digital reference object. Magn Reson Med 2020. doi:10.1002/mrm.27914.

DRO development

To achieve DSC-MRI signals representative of the temporal characteristics, magnitude, and distribution of contrast agent-induced T1 and T2 * changes observed across multiple glioblastomas, the DRO’s input parameters were trained using DSC-MRI data from 23 glioblastomas (40,000 voxels). The DRO’s ability to produce reliable signals across combinations of pulse sequence parameters and contrast agent dosing schemes unlike those in the training data set was validated by comparison with in vivo dual-echo DSC-MRI data acquired in a separate cohort of patients with glioblastomas. To achieve an excellent agreement between the DRO and in vivo data the training and validation process required a DRO consisting of 10 000 unique voxels.

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Localtab Group


Localtab
activetrue
titleData Access

Data Access

Click the  Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever . Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.


Data TypeDownload all or Query/FilterLicense
Images (DICOM, 4.5 GB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/display/Public/GBM-DSC-MRI-DRO?preview=%2F59310755%2F64685340%2FGBM-DSC-MRI-DRO_full.tcia



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urlhttps://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=GBM-DSC-MRI-DRO


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Localtab
titleDetailed Description

Detailed Description


Image Statistics

Radiology Imaging

Modalities

MR

Number of Participants

3

Number of Studies

31

Number of Series

364

Number of Images

47164

Images Size (GB)4.5


The field of view contains four regions of interest (ROI) per image. The settings for

  • Static field strength,
  • TR,
  • TE,
  • Flip angle,
  • and contrast dose

are included in the series description of each synthetic timecourse, as in Table 1 of the publication .

Note, do not sort by filename (chart at left), sort by Acquisition Number (chart at right). It should trace like the image on the right. Osirix, IBNeuro, ImageJ do this natively. If you are using Matlab or python please use DICOM header AcquisitionNumber (0020,0012) to reorder the files; using the file names could lead to a wrong signal profile.



Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy

Tcia license 4 international


Info
titleData Citation

Semmineh, N. B., Stokes, A. M., Bell, L. C., Boxerman, J. L., & Quarles, C. C. (2020). GBM-DSC-MRI-DRO [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2020.RMWVZWIX


Info
titlePublication Citation

Semmineh, N. B., Stokes, A. M., Bell, L. C., Boxerman, J. L., & Quarles, C. C. (2017). A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials. In Tomography (Vol. 3, Issue 1, pp. 41–49). MDPI AG. https://doi.org/10.18383/j.tom.2016.00286


Info
titleTCIA Citation

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057).  https://doi.org/10.1007/s10278-013-9622-7

Additional Publications related to this work: 

  • Semmineh, N. B., Bell, L. C., Stokes, A. M., Hu, L. S., Boxerman, J. L., & Quarles, C. C. (2018). Optimization of Acquisition and Analysis Methods for Clinical Dynamic Susceptibility Contrast MRI Using a Population-Based Digital Reference Object. In American Journal of Neuroradiology (Vol. 39, Issue 11, pp. 1981–1988). American Society of Neuroradiology (ASNR). https://doi.org/10.3174/ajnr.a5827
  • Bell, L. C., Semmineh, N., An, H., Eldeniz, C., Wahl, R., Schmainda, K. M., Prah, M. A., Erickson, B. J., Korfiatis, P., Wu, C., Sorace, A. G., Yankeelov, T. E., Rutledge, N., Chenevert, T. L., Malyarenko, D., Liu, Y., Brenner, A., Hu, L. S., Zhou, Y., … Quarles, C. C. (2019). Evaluating Multisite rCBV Consistency from DSC-MRI Imaging Protocols and Postprocessing Software Across the NCI Quantitative Imaging Network Sites Using a Digital Reference Object (DRO). In Tomography (Vol. 5, Issue 1, pp. 110–117). MDPI AG. https://doi.org/10.18383/j.tom.2018.00041
  • Stokes, A. M., Semmineh, N. B., Nespodzany, A., Bell, L. C., & Quarles, C. C. (2019). Systematic assessment of multi‐echo dynamic susceptibility contrast MRI using a digital reference object. In Magnetic Resonance in Medicine (Vol. 83, Issue 1, pp. 109–123). Wiley. https://doi.org/10.1002/mrm.27914

Other Publications Using This Data

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.


Localtab
titleVersions

Version 1 (Current): Updated 2022/07/21


Data TypeDownload all or Query/FilterLicense
Images (DICOM, 4.1 GB)5 GB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/display/Public/GBM-DSC-MRI-DRO?preview=%2F59310755%2F64685340%2FGBM-DSC-MRI-DRO_full.tcia



Tcia button generator
labelSearch
urlhttps://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=GBM-DSC-MRI-DRO


Image Removed Image Removed

(Requires 

Download requires the

NBIA Data Retriever.

)

CC BY 4.0

Data description (PDF)


Added new subjects.




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